applications

The growth of NoSQL continues to accelerate as the industry is increasingly forced to develop new and more specialized data structures to deal with the explosion of application and device data. At the same time, new data products for BI, Analytics, Reporting, Data Warehousing, AI, and Machine Learning continue along a similar growth trajectory. Enabling interoperability between applications and data sources, each with a unique interface and value proposition, is a tremendous challenge.
This paper discusses a variety of mapping and flattening techniques, and continues with examples that highlight performance and usability differences between approaches.

Learn how to get started with Apache Spark™
Apache Spark™’s ability to speed analytic applications by orders of magnitude, its versatility, and ease of use are quickly winning the market. With Spark’s appeal to developers, end users, and integrators to solve complex data problems at scale, it is now the most active open source project with the big data community.
With rapid adoption by enterprises across a wide range of industries, Spark has been deployed at massive scale, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. If you are a developer or data scientist interested in big data, learn how Spark may be the tool for you. Databricks is happy to present this ebook as a practical introduction to Spark.
Download this ebook to learn:
• Spark’s basic architecture
• Why Spark is a popular choice for data analytics
• What tools and features are available
• How to get started right away through interactive sample code

NoSQL database technology is increasingly chosen as viable alternative to relational databases, particularly for interactive web applications. Developers accustomed to the RDBMS structure and data models need to change their approach when transitioning to NoSQL. Download this white paper to learn about the main challenges that motivates the need for NoSQL, the differences between relational databases and distributed document-oriented databases, the key steps to perform document modeling in NoSQL databases, and how to handle concurrency, scaling and multiple-place updates in a non-relational database.

Interactive applications have changed dramatically over the last 15 years. In the late ‘90s,
large web companies emerged with dramatic increases in scale on many dimensions:
· The number of concurrent users skyrocketed as applications increasingly became accessible
· via the web (and later on mobile devices).
· The amount of data collected and processed soared as it became easier and increasingly
· valuable to capture all kinds of data.
· The amount of unstructured or semi-structured data exploded and its use became integral
· to the value and richness of applications.
Dealing with these issues was more and more difficult using relational database technology.
The key reason is that relational databases are essentially architected to run a single machine
and use a rigid, schema-based approach to modeling data.
Google, Amazon, Facebook, and LinkedIn were among the first companies to discover the serious
limitations of relational database technology for supporting these new application requirements.
Commercial alternatives didn’t exist, so they invented new data management approaches
themselves. Their pioneering work generated tremendous interest because a growing number of
companies faced similar problems. Open source NoSQL database projects formed to leverage the
work of the pioneers, and commercial companies associated with these projects soon followed.
Today, the use of NoSQL technology is rising rapidly among Internet companies and the
enterprise. It’s increasingly considered a viable alternative to relational databases, especially
as more organizations recognize that operating at scale is more effectively achieved running on
clusters of standard, commodity servers, and a schema-less data model is often a better approach
for handling the variety and type of data most often captured and processed today.

Modern enterprises face increasing pressure to deliver business value through technological innovation that leverages all available data. At the same time, those enterprises need to reduce expenses to stay competitive, deliver results faster to respond to market demands, use real-time analytics so users can make informed decisions, and develop new applications with enhanced developer productivity. All of these factors put big data at the top of the agenda.
Unfortunately, the promise of big data has often failed to deliver. With the growing volumes of unstructured and multi-structured data flooding into our data centers, the relational databases that enterprises have relied on for the last 40-years are now too limiting and inflexible. New-generation NoSQL (“Not Only SQL”) databases have gained popularity because they are ideally suited to deal with the volume, velocity, and variety of data that businesses and governments handle today.

This paper presents a practitioner informed roadmap intended to assist enterprises in maturing their Enterprise Information Management (EIM) practices, with a specific focus on improving Reference Data Management (RDM).
Reference data is found in every application used by an enterprise including back-end systems, front-end commerce applications, data exchange formats, and in outsourced, hosted systems, big data platforms, and data warehouses. It can easily be 20–50% of the tables in a data store. And the values are used throughout the transactional and mastered data sets to make the system internally consistent.

Add Big Data Technologies to Get More Value from Your Stack
Taking advantage of big data starts with understanding how to optimize and augment your existing infrastructure. Relational databases have endured for a reason – they fit well with the types of data that organizations use to run their business. These types of data in business applications such as ERP, CRM, EPM, etc., are not fundamentally changing, which suggests that relational databases will continue to play a foundational role in enterprise architectures for the foreseeable future. One area where emerging technologies can complement relational database technologies is big data. With the rapidly growing volumes of data, along with the many new sources of data, organizations look for ways to relieve pressure from their existing systems. That’s where Hadoop and NoSQL come in.

Legacy infrastructures simply cannot handle the workloads or power the applications that will drive business decisively forward in the years ahead. New infrastructure, new thinking and new approaches are in the offing, all driven by the mantra 'transform or die.'
This book is meant for IT architects; developers and development managers; platform architects; cloud specialists; and big data specialists. For you, the goal is to help create a sense of urgency you can present to your CXOs and others whose buy-in is needed to make essential infrastructure investments along the journey to digital transformation.

The database you pick for your next web or mobile application matters now more than ever. Today’s applications are expected to run non-stop and must efficiently manage continuously growing amounts of transactional and multi-structured data in order to do so. This has caused NoSQL to grow from a buzzword to a serious consideration for every database, from small shops to the enterprise. Read this whitepaper to learn why NoSQL databases have become such a popular option, explore the various types available, and assess whether you should consider implementing a NoSQL solution for your next application.

Emerging business innovations focused on realizing quick business value on new and growing data sources require “hybrid transactional and analytical processing” (HTAP), the notion of performing analysis on data directly in an operational data store. While this is not a new idea, Gartner reports that the potential for HTAP has not been fully realized due to technology limitations and inertia in IT departments. MemSQL offers a unique combination of performance, flexibility, and ease of use that allows companies to implement HTAP to power their business applications.

Many companies still use relational databases as part of the technology stack. However, others are innovating and incorporating NoSQL solutions and as a result they have simplified their deployments, enhanced their availability and reduced their costs.
In this whitepaper you will learn:
- Why companies choose Riak over a relational database.
- How to analyze the decision points you should consider when choosing between relational and Nosql databases
- Simple patters for building common applications in Riak using its key/value design
Learn how you can lead your organization into this new frontier.

The Internet of Things (IoT) or the Internet of Everything is changing the way companies interact with their customers and manage their data. These connected devices generate high volume time series data that can be created in milliseconds. This fast growth of IoT data and other time series data is producing challenges for enterprise applications where data must be collected, saved, and analyzed in the blink of an eye. Your application needs a database built to uniquely handle time series data to ensure your data is continuously available and accurate.Learn about the only NoSQL database optimized for IoT and Time Series data in this technical overview. Riak TS stores and analyzes massive amounts of data and is designed to be faster than Cassandra.

The demand for using data as an asset has grown to a level where data-centric applications are now the norm in enterprises. Yet data-centric applications fall short of user expectations at a high rate. Part of this is due to inadequate quality assurance. This in turn arises from trying to develop data-centric projects using the old paradigm of the SDLC, which came into existence during an age of process automation. SDLC does not fit with data-centric projects and cannot address the QA needs of these projects. Instead, a new approach is needed where analysts develop business rules to test atomic items of data quality. These rules have to be run in an automated fashion in a business rules engine. Additionally, QA has to be carried past the point of application implementation and support the running of the production environment.

What is fast data? It's data in motion, and it creates Big Data. But handling it requires a radically different approach. Download the Fast Data Stack white paper from VoltDB. Learn how to build fast data applications with an in-memory solution that’s powerful enough for real-time stateful operations.

The need for fast data applications is growing rapidly, driven by the IoT, the surge in machine-to-machine (M2M) data, global mobile device proliferation, and the monetization of SaaS platforms. So how do you combine real-time, streaming analytics with real-time decisions in an architecture that’s reliable, scalable, and simple?
In this report, Ryan Betts and John Hugg from VoltDB examine ways to develop apps for fast data, using pre-defined patterns. These patterns are general enough to suit both the do-it-yourself, hybrid batch/streaming approach, as well as the simpler, proven in-memory approach available with certain fast database offerings.

Reltio delivers reliable data, relevant insights and recommended actions so companies can be right faster. Reltio Cloud combines data-driven applications with modern data management for better planning, customer engagement and risk management.
IT streamlines data management for a complete view across all sources and formats at scale, while sales, marketing and compliance teams use data-driven applications to predict, collaborate and respond to opportunities in real-time.
Companies of all sizes, including leading Fortune 500 companies in healthcare and life sciences, distribution and retail rely on Reltio.

This whitepaper covers a recently completed a ground-breaking industry wide survey of executives, architects, and business stakeholders from data-driven organizations by Enterprise Management Research in order to explore the growing role of the CDO, and to explore the various data management maturity levels of enterprise companies.
This whitepaper explains how industry visionaries use data as an asset, and discusses the growing importance of data governance leadership. Additionally, it creates a data management maturity index to show how various companies match-up in their data management vision and capabilities. Finally, the whitepaper covers the top data-focused applications used and their average implementation timelines.

Increasing dependence on enterprise-class applications has created a demand for centralizing organizational data using techniques such as Master Data Management (MDM). The development of a useful MDM environment is often complicated by a lack of shared organizational information and data modeling. In this paper, David Loshin explores some of the root causes that have influenced an organization’s development of a variety of data models, how that organic development has introduced potential inconsistency in structure and semantics, and how those inconsistencies complicate master data integration.

In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used.
Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model.
Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.

In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used.
Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model.
Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.

The consumerization of IT requires an evolution in the way applications are designed and developed. This white paper looks at the requirements of the Fast Data workflow and proposes solution patterns for the most common problems software development organizations must resolve to build applications – and apps – capable of managing fast and big data.

Everyone in an organization relies on Metadata to do their jobs. Whenever an email is sent, a report is run, inventory is ordered, compliance procedures are verified, a new IT system is integrated, applications are executed, or essentially any other business function, process, or decision is undertaken, Metadata is facilitating in the background. If that Metadata is corrupt, missing, redundant, or unpredictable then they cannot do their jobs well, they cannot trust the data they are using, and the organization ultimately suffers at all levels. Data Stewards are the people who are use, define, cleanse, archive, analyze, and share the data that is mapped directly to the Metadata of their myriad database and application systems. If your organization does not have Data Stewards (or an inefficient Stewardship Program), you need them.
This paper is sponsored by: ASG.

Interactive software has changed in fundamental ways over the last 35 years. But until recently, database technology has not advanced to keep pace. The “online” systems of the 1970s have evolved into today’s web and mobile applications.

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